Numerous systems have been developed to overcome the complexity of determining when training and transition is to occur, and for which crew members, at which locations, at what times, and with an appropriate allocation of training resources including equipment and instructors.
The prior art systems have included both manual and automated systems with response times ranging from days, to weeks, and even months. Further, such systems have tended to implement a decision making process for providing a single solution, rather than a dynamic, adaptive, decision support system providing alternative solutions for evaluation by a strategic planner. In addition, such prior systems have been represented by models which either are too complex for commercial software solution, or have simplifying assumptions that make them too unrealistic for practical use. Prior systems also have generally been too costly in employee and equipment resources. See “Decision Support Systems-An application in strategic manpower planning of airline pilots” by Peter J. Verbeek, European Journal of Operational Research 55 (1991), pages 368-381, Elsevier Science Publishers B. V. While the Verbeek article does not disclose a decision support system, it does describe the enormous complexities which must be addressed in designing such a system to accommodate the large numbers of constraints and variables that are required for a solution to be realistic. Verbeek also referred to his own mixed integer model which was admittedly too complex for solution with commercial software, and thus too costly in time.
From the above it may be discerned that the problem of pilot staffing and training is one of the most complex and costly problems facing the major airlines. If not managed effectively, an airline cannot survive, not to mention profit, in the competitive air transportation market.
By way of example, Continental airlines provides both domestic and international service to more than 100 destinations around the world. They operate 325 aircraft of nine different fleet types to fly 1400 daily flights. Their 5000 pilots are stationed at three domestic and two international crew bases. At least twice a year Continental conducts a system bid award. These awards provide an opportunity for pilots to use their seniority to increase their pay and improve their work schedules by changing their position (base, fleet, and status), and a way for the airline to adjust staffing levels in response to retirements, attrition, and changes in their business plan. In an average system bid award, 15-20% of the airline's pilots receive new positions. The problem of taking the pilots who have received new positions, and finding a training class for each pilot requiring training, an advancement date for each pilot changing position without training, and a release date for each pilot leaving the airline, is a very large NP-hard problem to attempt to solve. Additional complexity for Continental comes from the facts that: pilot positions are interrelated; the timing and number of training classes is variable; minimal length student training schedules must be generated using limited resources; and numerous complicating regulations and business rules related to each pilot's seniority, flight history, and current and future position must be considered.
Continental manpower planners with expert knowledge took more than two weeks to manually generate a single, partial, sub-optimal training plan for ensuring adequate staffing levels with no detailed consideration of costs.
In contrast to the above prior art systems and methods, the system disclosed in related U.S. patent application Ser. No. 10/054,343, and assigned to the assignee of the present invention, is a realistic representation of the real world problem as evidenced by its implementation by Continental Airlines. The system is modeled so efficiently that it can be solved in under an hour. An hour is a huge improvement over the time required by the prior art systems, and is a very reasonable amount of time for a planning problem as complex as the one addressed in the generation of training and transition plans for all pilots of an entire airline.:
In response to a system bid award, the system manages large volumes of data, and employs state-of-the-art optimization modeling and solution techniques, to efficiently allocate human and training resources and attain optimal operational and cost effective performance. A training and transition plan is generated by the system which establishes the timing and number of pilot new hires, training assignments, advancements, and releases. The plan also provides the number of pilots whose training or release should be postponed, and the flow of pilots across different positions in a manner that ensures adequate staffing levels, minimum cost, and efficient utilization of training resources.
After an initial training and transition plan is established, crew planners often face changes in the airline operating environment which necessitate changes to the original plan. For example, in the event of a new bid award which occurs only a couple times each year, pilots may be scheduled for training and transition without regard to any existing plan. Events such as the following, however, often lead to changes in training and transition plans on as small as a monthly basis to maintain staffing levels: new market opportunities, the acquisition and retirement of aircraft and training resources, opening and closing sub-bases, and modification to the number of hours to be flown from different pilot positions to allow the airline to take advantage of business opportunities. Upon the occurrence of such change events, crew planners want to make as few adjustments as possible to the current training and transition plan to avoid disrupting the schedules of a large number of pilots.
The invention is an improvement over the system of U.S. patent application Ser. No. 10/054,343, and was created to overcome the challenges of timing the recall of pilots who have been furloughed from an airline, and also to limit any effect on current training and transition plans during the process of building a new plan.
Pilots who have been furloughed by the airline by contractual agreement must be brought back to the airline in seniority order, and must be brought back before any new pilots are hired by the airline. Two constraints are included in a mixed integer programming model (MIP Model) of the invention to determine when furloughed pilots will be recalled, and to ensure that contractual obligations are met in doing so. Two additional constraints are included to limit the percentage of pilots whose start bid periods are moved out of the bid period of the current plan, and to limit the total percentage of pilots whose start bid periods are either moved into or moved out of the bid period of the current plan.
Prior to the invention, crew planners used manual methods to provide solutions in overcoming the furloughed pilot recall and limited effect problems. Such manual methods were too time consuming for consideration of solution costs. Emphasis was placed mainly on staffing levels. Rather than hours to days being consumed in reaching a manual solution, the invention makes possible multiple alternative solutions in under one hour which address change events giving rise to a need to recall furloughed pilots, or to a need for limited start bid period changes to a current training and transition plan. In addition to the time savings, the solutions derived by this invention are optimized against the airline's costs while maintaining ideal staffing levels.